Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia

Abstract

Plasma phosphorylated tau181 (P-tau181) might be increased in Alzheimer’s disease (AD), but its usefulness for differential diagnosis and prognosis is unclear. We studied plasma P-tau181 in three cohorts, with a total of 589 individuals, including cognitively unimpaired participants and patients with mild cognitive impairment (MCI), AD dementia and non-AD neurodegenerative diseases. Plasma P-tau181 was increased in preclinical AD and further increased at the MCI and dementia stages. It correlated with CSF P-tau181 and predicted positive Tau positron emission tomography (PET) scans (area under the curve (AUC) = 0.87–0.91 for different brain regions). Plasma P-tau181 differentiated AD dementia from non-AD neurodegenerative diseases with an accuracy similar to that of Tau PET and CSF P-tau181 (AUC = 0.94–0.98), and detected AD neuropathology in an autopsy-confirmed cohort. High plasma P-tau181 was associated with subsequent development of AD dementia in cognitively unimpaired and MCI subjects. In conclusion, plasma P-tau181 is a noninvasive diagnostic and prognostic biomarker of AD, which may be useful in clinical practice and trials.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Association of plasma P-tau181 with CSF P-tau181, Tau PET and Aβ PET in cohort 1.
Fig. 2: Plasma P-tau181, CSF P-tau181 and Tau PET in different diagnostic groups.
Fig. 3: Plasma P-tau181 and progression to AD dementia.

Data availability

Anonymized data will be shared by request from any qualified investigator for the sole purpose of replicating procedures and results presented in the article, providing data transfer is in agreement with EU legislation on the general data protection regulation and decisions by the Ethical Review Board of Sweden and Region Skåne, which should be regulated in a material transfer agreement.

References

  1. 1.

    Jack, C. R. Jr. et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018).

  2. 2.

    Blennow, K., Hampel, H., Weiner, M. & Zetterberg, H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat. Rev. Neurol. 6, 131–144 (2010).

  3. 3.

    Mielke, M. M. et al. Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement. 14, 989–997 (2018).

  4. 4.

    Tatebe, H. et al. Quantification of plasma phosphorylated tau to use as a biomarker for brain Alzheimer pathology: pilot case-control studies including patients with Alzheimer’s disease and Down Syndrome. Mol. Neurodegener. 12, 63 (2017).

  5. 5.

    Yang, C. C. et al. Assay of plasma phosphorylated tau protein (Threonine 181) and total tau protein in early-stage Alzheimer’s disease. J. Alzheimers Dis. 61, 1323–1332 (2018).

  6. 6.

    Chouraki, V. et al. Plasma amyloid-beta and risk of Alzheimer’s disease in the Framingham Heart Study. Alzheimers Dement. 11, 249–257 e241 (2015).

  7. 7.

    Mattsson, N., Cullen, N. C., Andreasson, U., Zetterberg, H. & Blennow, K. Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 76, 791–799 (2019).

  8. 8.

    Mattsson, N. et al. Plasma tau in Alzheimer disease. Neurology 87, 1827–1835 (2016).

  9. 9.

    Pase, M. P. et al. Assessment of plasma total tau level as a predictive biomarker for dementia and related endophenotypes. JAMA Neurol. 76, 598–606 (2019).

  10. 10.

    Olsson, B. et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol. 15, 673–684 (2016).

  11. 11.

    Smith, R., Wibom, M., Pawlik, D., Englund, E. & Hansson, O. Correlation of in vivo [18F]Flortaucipir with postmortem Alzheimer disease tau pathology. JAMA Neurol. 76, 310–317 (2018).

  12. 12.

    Scholl, M. et al. Biomarkers for tau pathology. Mol Cell Neurosci. 97, 18–33 (2018).

  13. 13.

    Ossenkoppele, R. et al. Associations between tau, Abeta, and cortical thickness with cognition in Alzheimer disease. Neurology 92, e601–e612 (2019).

  14. 14.

    Smith, R. et al. 18F-AV-1451 tau PET imaging correlates strongly with tau neuropathology in MAPT mutation carriers. Brain 139, 2372–2379 (2016).

  15. 15.

    Palmqvist, S. et al. Performance of fully automated plasma assays as screening tests for Alzheimer disease-related beta-amyloid status. JAMA Neurol. 76, 1060–1069 (2019).

  16. 16.

    Mattsson, N. et al. The implications of different approaches to define ATN in Alzheimer’s disease. Neurology (in the press).

  17. 17.

    Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 280–292 (2011).

  18. 18.

    Palmqvist, S. et al. Accurate risk estimation of beta-amyloid positivity to identify prodromal Alzheimer’s disease: cross-validation study of practical algorithms. Alzheimers Dement. 15, 194–204 (2019).

  19. 19.

    Zetterberg, H. et al. Plasma tau levels in Alzheimer’s disease. Alzheimers Res. Ther. 5, 9 (2013).

  20. 20.

    Randall, J. et al. Tau proteins in serum predict neurological outcome after hypoxic brain injury from cardiac arrest: results of a pilot study. Resuscitation 84, 351–356 (2013).

  21. 21.

    Couchie, D. et al. Primary structure of high molecular weight tau present in the peripheral nervous system. Proc. Natl Acad. Sci. USA 89, 4378–4381 (1992).

  22. 22.

    Mattsson, N. et al. (18)F-AV-1451 and CSF T-tau and P-tau as biomarkers in Alzheimer’s disease. EMBO Mol. Med. 9, 1212–1223 (2017).

  23. 23.

    Mattsson, N. et al. Comparing (18)F-AV-1451 with CSF t-tau and p-tau for diagnosis of Alzheimer disease. Neurology 90, e388–e395 (2018).

  24. 24.

    Sato, C. et al. Tau kinetics in neurons and the human central nervous system. Neuron 97, 1284–1298 e1287 (2018).

  25. 25.

    Beach, T. G., Monsell, S. E., Phillips, L. E. & Kukull, W. Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005–2010. J. Neuropathol. Exp. Neurol. 71, 266–273 (2012).

  26. 26.

    Ossenkoppele, R. et al. Discriminative accuracy of [18F]flortaucipir positron emission tomography for Alzheimer disease vs other neurodegenerative disorders. JAMA 320, 1151–1162 (2018).

  27. 27.

    Hansson, O. et al. Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol. 5, 228–234 (2006).

  28. 28.

    Nakamura, A. et al. High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature 554, 249–254 (2018).

  29. 29.

    Schindler, S. E. et al. High-precision plasma beta-amyloid 42/40 predicts current and future brain amyloidosis. Neurology 93, e1647–e1659 (2019).

  30. 30.

    Kuhlmann, J. et al. CSF Abeta1-42 – an excellent but complicated Alzheimer’s biomarker – a route to standardisation. Clin. Chim. Acta 467, 27–33 (2017).

  31. 31.

    Barthélemy, N. R. et al. Tau hyperphosphorylation on T217 in cerebrospinal fluid is specifically associated to amyloid-β pathology. Preprint at bioRxiv https://doi.org/10.1101/226977 (2017).

  32. 32.

    Rissin, D. M. et al. Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. Nat. Biotechnol. 28, 595–599 (2010).

  33. 33.

    Petersen, R. C. Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256, 183–194 (2004).

  34. 34.

    Mattsson, N. et al. Increased amyloidogenic APP processing in APOE varepsilon4-negative individuals with cerebral beta-amyloidosis. Nat. Commun. 7, 10918 (2016).

  35. 35.

    Gelb, D. J., Oliver, E. & Gilman, S. Diagnostic criteria for Parkinson disease. Arch. Neurol. 56, 33–39 (1999).

  36. 36.

    Hoglinger, G. U. et al. Clinical diagnosis of progressive supranuclear palsy: the Movement Disorder Society criteria. Mov. Disord. 32, 853–864 (2017).

  37. 37.

    Litvan, I. et al. Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele–Richardson–Olszewski Syndrome): report of the NINDS-SPSP International Workshop. Neurology 47, 1–9 (1996).

  38. 38.

    Armstrong, M. J. et al. Criteria for the diagnosis of corticobasal degeneration. Neurology 80, 496–503 (2013).

  39. 39.

    The National Institute on Aging, and Reagan Institute Working Group on Diagnostic Criteria for the Neuropathological Assessment of Alzheimer’s Disease. Consensus recommendations for the postmortem diagnosis of Alzheimer’s disease. Neurobiol. Aging 18, S1–S2 (1997).

  40. 40.

    Palmqvist, S. et al. Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid beta-amyloid 42: a cross-validation study against amyloid positron emission tomography. JAMA Neurol. 71, 1282–1289 (2014).

  41. 41.

    Hansson, O. et al. Blood-based NfL: a biomarker for differential diagnosis of parkinsonian disorder. Neurology 88, 930–937 (2017).

  42. 42.

    Hahn, A. et al. Modeling strategies for quantification of in vivo (18)F-AV-1451 binding in patients with tau pathology. J. Nucl. Med. 58, 623–631 (2017).

  43. 43.

    Maass, A. et al. Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer’s disease. Neuroimage 157, 448–463 (2017).

  44. 44.

    Cho, H. et al. In vivo cortical spreading pattern of tau and amyloid in the Alzheimer disease spectrum. Ann. Neurol. 80, 247–258 (2016).

  45. 45.

    Van Essen, D. C. A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex. Neuroimage 28, 635–662 (2005).

  46. 46.

    Palmqvist, S. et al. Earliest accumulation of beta-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat. Commun. 8, 1214 (2017).

Download references

Acknowledgements

The work in the present study was supported by the European Research Council (grant no. 311292 to O.H), the Swedish Research Council (grant no. 2016-00906 to O.H), the Knut and Alice Wallenberg foundation (grant no. 2017-0383), the Marianne and Marcus Wallenberg foundation (grant no. 2015.0125 to O.H), the Swedish Alzheimer Foundation (grant nos. AF-745911 to O.H, AF-843991 to N.M. and AF-846521 to S.J.), the Swedish Brain Foundation (grant nos. FO2019-0326 to O.H. and FO2019-0029 to N.M.), the Swedish Medical Association (grant no. SLS-780901 to N.M.), the Parkinson Foundation of Sweden (grant no. 1127718 to O.H), the Skåne University Hospital Foundation (grant no. 2019-o000032 to O.H) and the Swedish federal government under the ALF agreement (grant no. 2018-Projekt0279 to O.H) and The Medical Faculty at Lund University and Region Skåne (Wallenberg Molecular Medicine Fellow grant 2019 to N.M.). E.M.R., T.G.B. and their research programs (the Arizona Alzheimer’s Disease Center, Arizona Alzheimer’s Consortium and Arizona Study of Aging and Neurodegenerative Disorders) have been supported by the US National Institutes of Health (nos. U24 NS072026 and P30 AG19610), Arizona Department of Health Services (contract no. 211002), Arizona Biomedical Research Commission (contract nos. 4001, 0011, 05-901 and 1001) and the Michael J. Fox Foundation for Parkinson’s Research. Doses of [18F]flutemetamol injection were sponsored by GE Healthcare. The precursor of 18F-flortaucipir was provided by AVID radiopharmaceuticals.

Author information

S.J., N.M., S.P., R.S., T.G.B., G.E.S., X.C., N.K.P., U.E., H.Z., K.B., E.M.R, E.S., J.L.D. and O.H. collected the data and reviewed the manuscript for intellectual content. S.J, N.M. and O.H. analyzed and interpreted the data, prepared figures and co-wrote the manuscript. O.H. was the principal designer and coordinator of the study and overviewed collection, analysis and interpretation of the study data.

Correspondence to Shorena Janelidze or Oskar Hansson.

Ethics declarations

Competing interests

S.J., S.P., E.S. and G.E.S. report no conflicts of interest. N.M. has been a consultant for ADNI. R.S. has served as a (nonpaid) consultant for Roche. H.Z. has served at scientific advisory boards for Roche Diagnostics, Wave, Samumed and CogRx, has given lectures in symposia sponsored by Alzecure and Biogen and is a co-founder of Brain Biomarker Solutions, a GU Ventures-based platform company at the University of Gothenburg, all unrelated to the work presented in this paper. K.B. has served as a consultant or at advisory boards for Alector, Biogen, CogRx, Lilly, MagQu, Novartis and Roche Diagnostics, and is a co-founder of Brain Biomarker Solutions, all unrelated to the work presented in this paper. E.M.R. is a scientific advisor to Alzheon, Aural Analytics, Denali, Green Valley, MaQ and United Neuroscience, and to Roche and Roche Diagnostics (compensation for travel only). His NIH-supported studies include research contracts with Avid/Eli Lilly, Genentech/Roche and Novartis/Amgen. O.H. has acquired research support (for the institution) from Roche, GE Healthcare, Biogen, AVID Radiopharmaceuticals and Euroimmun. In the past 2 years he has received consultancy/speaker fees (paid to the institution) from Biogen and Roche. T.G.B. has served on a scientific advisory board and has been a consultant for Vivid Genomics and Prothena Biosciences. The sponsors mentioned above had no role in the design and conduct of the study, collection, management, analysis and interpretation of the data, nor in preparation, review and approval of the manuscript. U.E. is an employee of the Roche Group. X.C., N.K.P. and J.L.D. are employees of Eli Lilly and Company.

Additional information

Peer review information Brett Benedetti and Kate Gao were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Association of plasma P-tau181 with CSF P-tau181 and Aβ PET.

(a) Association between plasma and CSF P-tau181 in cohort 2 (n = 343 [Aβ+ n = 173; Aβ- n = 170]). (b) Association between plasma P-tau181 and Aβ PET in cohort 2 (n = 324 [Aβ+ n = 164; Aβ- n = 160]). (c) Association between plasma and CSF P-tau181 in Aβ+ cognitively unimpaired in cohort 1 (n = 37). (d) Association between plasma and CSF P-tau181 in Aβ+ cognitively unimpaired in cohort 2 (n = 93). Data are shown as β (standardized coefficient) and p value from linear regression adjusted for age and sex as covariates; linear regression lines with 95% CI (shaded regions) are from unadjusted models. Aβ positivity was defined using CSF Aβ42 cutoff value of < 510 pg/ml and Aβ PET SUVR cutoff value of > 0.743. Study participants who underwent both lumbar puncture and Aβ PET imaging were considered Aβ positive if either CSF Aβ42 or Aβ PET measures were abnormal.

Extended Data Fig. 2 Plasma P-tau181 and Tau PET in cohort 1.

Associations between plasma P-tau181 and Tau PET (n = 174 [Aβ+ n = 124; Aβ- n = 48; Aβ undefined n = 2] in a priori defined brain regions linked to tau pathology in AD, the Braak I-II ROI (a), III-IV ROI (b), V-VI ROI (c) and inferior temporal cortex ROI (d). Data are shown as β (standardized coefficient) and p value from linear regression adjusted for age and sex; linear regression lines with 95% CI (shaded regions) are from unadjusted models. Aβ positivity was defined using CSF Aβ42 cutoff value of < 510 pg/ml and Aβ PET SUVR cutoff value of > 0.743. Study participants who underwent both lumbar puncture and Aβ PET imaging were considered Aβ positive if either CSF Aβ42 or Aβ PET measures were abnormal. Two study participants with undefined Aβ status are represented as red x points. AD = Alzheimer disease; Aβ+ = Amyloid-β positive; Aβ- = Amyloid-β negative; MCI = mild cognitive impairment; PET = positron emission tomography; ITC = inferior temporal cortex; ROI = region of interest; SUVR = standardized uptake value ratio.

Extended Data Fig. 3 Scatter plots of plasma P-tau181.

(a) Scatter plot for Fig. 2a. (b) Scatter plot for Fig. 2b. (c) Scatter plot for Fig. 2d. (d) Scatter plot for Fig. 2e. (e) Scatter plot for Fig. 3a. P-values are from univariate general linear models adjusted for age and sex and additionally for years of education in (e) as described in the methods. Solid horizontal lines represent median and error bars correspond to interquartile range; dashed horizontal lines indicated median in the Tau PET negative group (a, b), Aβ- CU group (c, d) and Aβ- group (e).

Extended Data Fig. 4 Plasma P-tau181 in cohort 3.

(a) Plasma concentrations of P-tau181 in cases with AD dementia and high likelihood that dementia was due to AD histopathology according to NIA-Reagan criteria versus individuals with no or sparse neuritic plaques. P-values are from univariate general linear models adjusted for age and sex; boxes show interquartile range, the horizontal lines are medians and the whiskers were plotted using Tukey method. (b) ROC curve analyses for distinguishing the AD dementia group (n = 16) from non-AD group (n = 47). AD = Alzheimer disease; AUC = area under the curve; CI = confidence interval; NIA = NIA-R, National Institute on Aging-Reagan Institute Working Group; ROC = receiver operating characteristic.

Extended Data Fig. 5 Plasma P-tau181 and Aβ PET.

Plasma P-tau181 in relation to global cortical Aβ load in cohort 1 (a, n = 129) and cohort 2 (b, n = 324). The solid lines are fits from spline models of P-tau181 on [18F]flutemetamol. Associations between plasma P-tau181 (log-transformed) and continuous Aβ PET uptake were tested with non-linear polynomial spline models (using I-spline basis), to detect Aβ PET thresholds for increased P-tau181 (the Aβ level where the spline increased at least two standard errors from baseline). The shaded area represents 95% CI. The thick dotted line shows an a priori Aβ PET threshold for Aβ PET positivity (0.743 SUVR). The thin dotted lines indicate the [18F]flutemetamol level where plasma P-tau181 is significantly increased from baseline. PET = positron emission tomography; SUVR = standardized uptake value ratio.

Extended Data Fig. 6 Plasma P-tau181 and progression to AD dementia.

Boxplot (a) and scatterplot (b) for plasma P-tau181 concentrations in Aβ+ and Aβ- cognitively unimpaired (CU) and MCI patients who did not develop AD dementia or developed AD dementia during clinical follow-up. No participants from the Aβ- CU group progressed to AD dementia. Data from one Aβ-MCI who converted to AD dementia was excluded from the figure. P-values are from univariate general linear models adjusted for age and sex as described in the methods. In (a) boxes show interquartile range, the horizontal lines are medians and the whiskers were plotted using Tukey method; in (b) solid horizontal lines represent median, error bars correspond to interquartile range and dashed horizontal lines indicated median in the Aβ- CU group. AD = Alzheimer disease; Aβ+ = Amyloid-β positive; Aβ- = Amyloid-β negative; MCI = Mild cognitive impairment.

Supplementary information

Supplementary Information

Supplementary Results, Tables 1–6 and STARD checklist

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Janelidze, S., Mattsson, N., Palmqvist, S. et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat Med 26, 379–386 (2020). https://doi.org/10.1038/s41591-020-0755-1

Download citation

Further reading